{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用Stocker类分析股票的基本示例。Stocker类建立在quandl财务库和fbprophet附加模型之上"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 需要保证analysis和stocker.py在同一个文件夹"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from stocker import Stocker"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 对象是Python类的实例，要创建一个存储对象，使用有效的股票代码调用stocker类（约3000个），在这个过程中需要使用Microsoft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSFT Stocker Initialized. Data covers 1986-03-13 00:00:00 to 2018-03-27 00:00:00.\n"
     ]
    }
   ],
   "source": [
    "microsoft = Stocker('MSFT')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### stocker对象包含许多属性或数据片段以及作用于该数据的方法和函数。一个属性是股票历史的数据，我们可以把它分配给变量，然后查看数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Ex-Dividend</th>\n",
       "      <th>Split Ratio</th>\n",
       "      <th>Adj. Open</th>\n",
       "      <th>Adj. High</th>\n",
       "      <th>Adj. Low</th>\n",
       "      <th>Adj. Close</th>\n",
       "      <th>Adj. Volume</th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "      <th>Daily Change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1986-03-13</td>\n",
       "      <td>25.50</td>\n",
       "      <td>29.25</td>\n",
       "      <td>25.5</td>\n",
       "      <td>28.00</td>\n",
       "      <td>3582600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.058941</td>\n",
       "      <td>0.067609</td>\n",
       "      <td>0.058941</td>\n",
       "      <td>0.064720</td>\n",
       "      <td>1.031789e+09</td>\n",
       "      <td>1986-03-13</td>\n",
       "      <td>0.064720</td>\n",
       "      <td>0.005779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1986-03-14</td>\n",
       "      <td>28.00</td>\n",
       "      <td>29.50</td>\n",
       "      <td>28.0</td>\n",
       "      <td>29.00</td>\n",
       "      <td>1070000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.064720</td>\n",
       "      <td>0.068187</td>\n",
       "      <td>0.064720</td>\n",
       "      <td>0.067031</td>\n",
       "      <td>3.081600e+08</td>\n",
       "      <td>1986-03-14</td>\n",
       "      <td>0.067031</td>\n",
       "      <td>0.002311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1986-03-17</td>\n",
       "      <td>29.00</td>\n",
       "      <td>29.75</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.50</td>\n",
       "      <td>462400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.067031</td>\n",
       "      <td>0.068765</td>\n",
       "      <td>0.067031</td>\n",
       "      <td>0.068187</td>\n",
       "      <td>1.331712e+08</td>\n",
       "      <td>1986-03-17</td>\n",
       "      <td>0.068187</td>\n",
       "      <td>0.001156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1986-03-18</td>\n",
       "      <td>29.50</td>\n",
       "      <td>29.75</td>\n",
       "      <td>28.5</td>\n",
       "      <td>28.75</td>\n",
       "      <td>235300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.068187</td>\n",
       "      <td>0.068765</td>\n",
       "      <td>0.065876</td>\n",
       "      <td>0.066454</td>\n",
       "      <td>6.776640e+07</td>\n",
       "      <td>1986-03-18</td>\n",
       "      <td>0.066454</td>\n",
       "      <td>-0.001734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1986-03-19</td>\n",
       "      <td>28.75</td>\n",
       "      <td>29.00</td>\n",
       "      <td>28.0</td>\n",
       "      <td>28.25</td>\n",
       "      <td>166300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.066454</td>\n",
       "      <td>0.067031</td>\n",
       "      <td>0.064720</td>\n",
       "      <td>0.065298</td>\n",
       "      <td>4.789440e+07</td>\n",
       "      <td>1986-03-19</td>\n",
       "      <td>0.065298</td>\n",
       "      <td>-0.001156</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date   Open   High   Low  Close     Volume  Ex-Dividend  Split Ratio  \\\n",
       "0 1986-03-13  25.50  29.25  25.5  28.00  3582600.0          0.0          1.0   \n",
       "1 1986-03-14  28.00  29.50  28.0  29.00  1070000.0          0.0          1.0   \n",
       "2 1986-03-17  29.00  29.75  29.0  29.50   462400.0          0.0          1.0   \n",
       "3 1986-03-18  29.50  29.75  28.5  28.75   235300.0          0.0          1.0   \n",
       "4 1986-03-19  28.75  29.00  28.0  28.25   166300.0          0.0          1.0   \n",
       "\n",
       "   Adj. Open  Adj. High  Adj. Low  Adj. Close   Adj. Volume         ds  \\\n",
       "0   0.058941   0.067609  0.058941    0.064720  1.031789e+09 1986-03-13   \n",
       "1   0.064720   0.068187  0.064720    0.067031  3.081600e+08 1986-03-14   \n",
       "2   0.067031   0.068765  0.067031    0.068187  1.331712e+08 1986-03-17   \n",
       "3   0.068187   0.068765  0.065876    0.066454  6.776640e+07 1986-03-18   \n",
       "4   0.066454   0.067031  0.064720    0.065298  4.789440e+07 1986-03-19   \n",
       "\n",
       "          y  Daily Change  \n",
       "0  0.064720      0.005779  \n",
       "1  0.067031      0.002311  \n",
       "2  0.068187      0.001156  \n",
       "3  0.066454     -0.001734  \n",
       "4  0.065298     -0.001156  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_history = microsoft.stock\n",
    "stock_history.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### High是股票当天的最高价 Low是股票当天的最低价\n",
    "#### Open是当天开始的价格 Close是闭市时间的股票价格\n",
    "#### Volume是交易数量  Ex-Dividend除息  Split Ratio股票分割\n",
    "#### Adjust Open开市价格；Adjust High人为调整最高；Adjust Low人为最低；Adjust Close根据法人行为调整之后的闭市价格；Adjust Volume人为交易数量"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 类的优点是数据和作用于数据的函数与单个变量相关联。实际上，类是一种超级数据结构因为它包含其他数据和函数。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 股票历史数据的统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Adj. Close',\n",
       " 96.77,\n",
       " Timestamp('2018-03-12 00:00:00'),\n",
       " 0.060097123934714,\n",
       " Timestamp('1986-03-24 00:00:00'),\n",
       " 89.47,\n",
       " Timestamp('2018-03-27 00:00:00'))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "microsoft.plot_stock()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum Daily Change = 4.44 on 2018-02-06 00:00:00.\n",
      "Minimum Daily Change = -5.47 on 2018-03-27 00:00:00.\n",
      "Current Daily Change = -5.47 on 2018-03-27 00:00:00.\n",
      "\n",
      "Maximum Adj. Volume = 169163953.00 on 2015-01-27 00:00:00.\n",
      "Minimum Adj. Volume = 7425503.00 on 2017-11-24 00:00:00.\n",
      "Current Adj. Volume = 53704562.00 on 2018-03-27 00:00:00.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1da55ec1128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "microsoft.plot_stock(start_date = '2000-01-03', end_date = '2018-01-16', \n",
    "                     stats = ['Daily Change', 'Adj. Volume'], plot_type='pct')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 假设有100美元股票的时候开始投资"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSFT Total buy and hold profit from 1986-03-13 00:00:00 to 2018-01-16 00:00:00 for 100 shares = $8829.11\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "float() argument must be a string or a number, not 'Timestamp'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    339\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    340\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    342\u001b[0m             \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m    236\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    237\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 238\u001b[1;33m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'png'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    239\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'retina'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m'png2x'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    240\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m    120\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    121\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 122\u001b[1;33m     \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    123\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    124\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'svg'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[0;32m   2214\u001b[0m                     \u001b[0morientation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2215\u001b[0m                     \u001b[0mdryrun\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2216\u001b[1;33m                     **kwargs)\n\u001b[0m\u001b[0;32m   2217\u001b[0m                 \u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2218\u001b[0m                 \u001b[0mbbox_inches\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[1;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[0;32m    505\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    506\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mprint_png\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilename_or_obj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 507\u001b[1;33m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    508\u001b[0m         \u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    509\u001b[0m         \u001b[0moriginal_dpi\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    428\u001b[0m             \u001b[1;31m# if toolbar:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    429\u001b[0m             \u001b[1;31m#     toolbar.set_cursor(cursors.WAIT)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 430\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    431\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    432\u001b[0m             \u001b[1;31m# if toolbar:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m   1297\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1298\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[1;32m-> 1299\u001b[1;33m                 renderer, self, artists, self.suppressComposite)\n\u001b[0m\u001b[0;32m   1300\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1301\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'figure'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[0;32m    136\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    137\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 138\u001b[1;33m             \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    139\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    140\u001b[0m         \u001b[1;31m# Composite any adjacent images together\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     54\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 55\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     56\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     57\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer, inframe)\u001b[0m\n\u001b[0;32m   2435\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2436\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2437\u001b[1;33m         \u001b[0mmimage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0martists\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2438\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2439\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'axes'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[0;32m    136\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    137\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 138\u001b[1;33m             \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    139\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    140\u001b[0m         \u001b[1;31m# Composite any adjacent images together\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     54\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 55\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     56\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     57\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\text.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m    713\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    714\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0m_wrap_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mtextobj\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 715\u001b[1;33m             \u001b[0mbbox\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minfo\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdescent\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtextobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_layout\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    716\u001b[0m             \u001b[0mtrans\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtextobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    717\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\text.py\u001b[0m in \u001b[0;36m_get_layout\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m    288\u001b[0m         \u001b[0mof\u001b[0m \u001b[0ma\u001b[0m \u001b[0mrotated\u001b[0m \u001b[0mtext\u001b[0m \u001b[0mwhen\u001b[0m \u001b[0mnecessary\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    289\u001b[0m         \"\"\"\n\u001b[1;32m--> 290\u001b[1;33m         \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_prop_tup\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    291\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_cached\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    292\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_cached\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\text.py\u001b[0m in \u001b[0;36mget_prop_tup\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m    871\u001b[0m         \u001b[0mneed\u001b[0m \u001b[0mto\u001b[0m \u001b[0mknow\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mtext\u001b[0m \u001b[0mhas\u001b[0m \u001b[0mchanged\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    872\u001b[0m         \"\"\"\n\u001b[1;32m--> 873\u001b[1;33m         \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_unitless_position\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    874\u001b[0m         \u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrenderer\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_renderer\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    875\u001b[0m         return (x, y, self.get_text(), self._color,\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\text.py\u001b[0m in \u001b[0;36mget_unitless_position\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    853\u001b[0m         \u001b[1;31m# This will get the position with all unit information stripped away.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    854\u001b[0m         \u001b[1;31m# This is here for convienience since it is done in several locations.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 855\u001b[1;33m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert_xunits\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_x\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    856\u001b[0m         \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert_yunits\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_y\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    857\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: float() argument must be a string or a number, not 'Timestamp'"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1957dfbce80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "microsoft.buy_and_hold(start_date='1986-03-13', end_date='2018-01-16', nshares=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSFT Total buy and hold profit from 1999-01-05 00:00:00 to 2002-01-03 00:00:00 for 100 shares = $-56.92\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "float() argument must be a string or a number, not 'Timestamp'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    339\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    340\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    342\u001b[0m             \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m    236\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    237\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 238\u001b[1;33m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'png'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    239\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'retina'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m'png2x'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    240\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m    120\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    121\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 122\u001b[1;33m     \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    123\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    124\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'svg'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[0;32m   2214\u001b[0m                     \u001b[0morientation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2215\u001b[0m                     \u001b[0mdryrun\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2216\u001b[1;33m                     **kwargs)\n\u001b[0m\u001b[0;32m   2217\u001b[0m                 \u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2218\u001b[0m                 \u001b[0mbbox_inches\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[1;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[0;32m    505\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    506\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mprint_png\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilename_or_obj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 507\u001b[1;33m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    508\u001b[0m         \u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    509\u001b[0m         \u001b[0moriginal_dpi\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    428\u001b[0m             \u001b[1;31m# if toolbar:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    429\u001b[0m             \u001b[1;31m#     toolbar.set_cursor(cursors.WAIT)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 430\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    431\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    432\u001b[0m             \u001b[1;31m# if toolbar:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m   1297\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1298\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[1;32m-> 1299\u001b[1;33m                 renderer, self, artists, self.suppressComposite)\n\u001b[0m\u001b[0;32m   1300\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1301\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'figure'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[0;32m    136\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    137\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 138\u001b[1;33m             \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    139\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    140\u001b[0m         \u001b[1;31m# Composite any adjacent images together\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     54\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 55\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     56\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     57\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer, inframe)\u001b[0m\n\u001b[0;32m   2435\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2436\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2437\u001b[1;33m         \u001b[0mmimage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0martists\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2438\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2439\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'axes'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[0;32m    136\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    137\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 138\u001b[1;33m             \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    139\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    140\u001b[0m         \u001b[1;31m# Composite any adjacent images together\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     54\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 55\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     56\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     57\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\text.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m    713\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    714\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0m_wrap_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mtextobj\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 715\u001b[1;33m             \u001b[0mbbox\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minfo\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdescent\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtextobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_layout\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    716\u001b[0m             \u001b[0mtrans\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtextobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    717\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\text.py\u001b[0m in \u001b[0;36m_get_layout\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m    288\u001b[0m         \u001b[0mof\u001b[0m \u001b[0ma\u001b[0m \u001b[0mrotated\u001b[0m \u001b[0mtext\u001b[0m \u001b[0mwhen\u001b[0m \u001b[0mnecessary\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    289\u001b[0m         \"\"\"\n\u001b[1;32m--> 290\u001b[1;33m         \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_prop_tup\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    291\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_cached\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    292\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_cached\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\text.py\u001b[0m in \u001b[0;36mget_prop_tup\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m    871\u001b[0m         \u001b[0mneed\u001b[0m \u001b[0mto\u001b[0m \u001b[0mknow\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mtext\u001b[0m \u001b[0mhas\u001b[0m \u001b[0mchanged\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    872\u001b[0m         \"\"\"\n\u001b[1;32m--> 873\u001b[1;33m         \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_unitless_position\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    874\u001b[0m         \u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrenderer\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_renderer\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    875\u001b[0m         return (x, y, self.get_text(), self._color,\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\text.py\u001b[0m in \u001b[0;36mget_unitless_position\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    853\u001b[0m         \u001b[1;31m# This will get the position with all unit information stripped away.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    854\u001b[0m         \u001b[1;31m# This is here for convienience since it is done in several locations.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 855\u001b[1;33m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert_xunits\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_x\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    856\u001b[0m         \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert_yunits\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_y\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    857\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: float() argument must be a string or a number, not 'Timestamp'"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1957da772e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "microsoft.buy_and_hold(start_date='1999-01-05', end_date='2002-01-03', nshares=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 附加模型将时间序列表示为不同时间尺度（年度，月度，季节性）的总体趋势和模式。 虽然微软的整体方向是积极的，但它可能会在每个星期二减少，如果这是真的，我们可以利用我们在股票市场上的优势。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Downloads\\Anaconda\\lib\\site-packages\\pystan\\misc.py:399: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  elif np.issubdtype(np.asarray(v).dtype, float):\n"
     ]
    },
    {
     "ename": "OverflowError",
     "evalue": "Python int too large to convert to C long",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mOverflowError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    339\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    340\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    342\u001b[0m             \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m    236\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    237\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 238\u001b[1;33m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'png'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    239\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'retina'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m'png2x'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    240\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m    120\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    121\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 122\u001b[1;33m     \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    123\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    124\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'svg'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[0;32m   2214\u001b[0m                     \u001b[0morientation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2215\u001b[0m                     \u001b[0mdryrun\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2216\u001b[1;33m                     **kwargs)\n\u001b[0m\u001b[0;32m   2217\u001b[0m                 \u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2218\u001b[0m                 \u001b[0mbbox_inches\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[1;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[0;32m    505\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    506\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mprint_png\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilename_or_obj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 507\u001b[1;33m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    508\u001b[0m         \u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    509\u001b[0m         \u001b[0moriginal_dpi\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    428\u001b[0m             \u001b[1;31m# if toolbar:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    429\u001b[0m             \u001b[1;31m#     toolbar.set_cursor(cursors.WAIT)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 430\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    431\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    432\u001b[0m             \u001b[1;31m# if toolbar:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     54\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 55\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     56\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     57\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m   1297\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1298\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[1;32m-> 1299\u001b[1;33m                 renderer, self, artists, self.suppressComposite)\n\u001b[0m\u001b[0;32m   1300\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1301\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'figure'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[0;32m    136\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    137\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 138\u001b[1;33m             \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    139\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    140\u001b[0m         \u001b[1;31m# Composite any adjacent images together\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     54\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 55\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     56\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     57\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer, inframe)\u001b[0m\n\u001b[0;32m   2435\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2436\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2437\u001b[1;33m         \u001b[0mmimage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0martists\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2438\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2439\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'axes'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[0;32m    136\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    137\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 138\u001b[1;33m             \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    139\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    140\u001b[0m         \u001b[1;31m# Composite any adjacent images together\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     54\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 55\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     56\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     57\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1131\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen_group\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1132\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1133\u001b[1;33m         \u001b[0mticks_to_draw\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_update_ticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1134\u001b[0m         ticklabelBoxes, ticklabelBoxes2 = self._get_tick_bboxes(ticks_to_draw,\n\u001b[0;32m   1135\u001b[0m                                                                 renderer)\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36m_update_ticks\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m    972\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    973\u001b[0m         \u001b[0minterval\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_view_interval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 974\u001b[1;33m         \u001b[0mtick_tups\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miter_ticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    975\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_smart_bounds\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mtick_tups\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    976\u001b[0m             \u001b[1;31m# handle inverted limits\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36miter_ticks\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    915\u001b[0m         \u001b[0mIterate\u001b[0m \u001b[0mthrough\u001b[0m \u001b[0mall\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mmajor\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mminor\u001b[0m \u001b[0mticks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    916\u001b[0m         \"\"\"\n\u001b[1;32m--> 917\u001b[1;33m         \u001b[0mmajorLocs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlocator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    918\u001b[0m         \u001b[0mmajorTicks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_major_ticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmajorLocs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    919\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_locs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmajorLocs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\dates.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1095\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1096\u001b[0m         \u001b[1;34m'Return the locations of the ticks'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1097\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrefresh\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1098\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_locator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1099\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\dates.py\u001b[0m in \u001b[0;36mrefresh\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1115\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mrefresh\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1116\u001b[0m         \u001b[1;34m'Refresh internal information based on current limits.'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1117\u001b[1;33m         \u001b[0mdmin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdmax\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mviewlim_to_dt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1118\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_locator\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_locator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdmin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdmax\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1119\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\dates.py\u001b[0m in \u001b[0;36mviewlim_to_dt\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    873\u001b[0m             \u001b[0mvmin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmax\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvmax\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    874\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 875\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mnum2date\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtz\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnum2date\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvmax\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtz\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    876\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    877\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_unit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\dates.py\u001b[0m in \u001b[0;36mnum2date\u001b[1;34m(x, tz)\u001b[0m\n\u001b[0;32m    464\u001b[0m         \u001b[0mtz\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_get_rc_timezone\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    465\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miterable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 466\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_from_ordinalf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtz\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    467\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    468\u001b[0m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\dates.py\u001b[0m in \u001b[0;36m_from_ordinalf\u001b[1;34m(x, tz)\u001b[0m\n\u001b[0;32m    277\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    278\u001b[0m     \u001b[0mix\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 279\u001b[1;33m     \u001b[0mdt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfromordinal\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mix\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtzinfo\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mUTC\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    280\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    281\u001b[0m     \u001b[0mremainder\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mix\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mOverflowError\u001b[0m: Python int too large to convert to C long"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1957f1480f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model, model_data = microsoft.create_prophet_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x195031625f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.plot_components(model_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 在过去三年里，整体趋势明显是向上的。一年内的趋势似乎是7月、9月和10月有所下降，12月和1月增幅最大。随着时间尺度的减小，模式变得更加复杂。如果认为可能存在有意义的周趋势，可以通过修改Stocker对象上的相关属性来添加周季节性组件。然后重新创建模型并绘制组件。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "print(microsoft.weekly_seasonality)\n",
    "microsoft.weekly_seasonality = True\n",
    "print(microsoft.weekly_seasonality)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Downloads\\Anaconda\\lib\\site-packages\\pystan\\misc.py:399: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  elif np.issubdtype(np.asarray(v).dtype, float):\n"
     ]
    },
    {
     "ename": "OverflowError",
     "evalue": "Python int too large to convert to C long",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mOverflowError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    339\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    340\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    342\u001b[0m             \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m    236\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    237\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 238\u001b[1;33m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'png'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    239\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'retina'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m'png2x'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    240\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m    120\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    121\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 122\u001b[1;33m     \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    123\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    124\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'svg'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[0;32m   2214\u001b[0m                     \u001b[0morientation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2215\u001b[0m                     \u001b[0mdryrun\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2216\u001b[1;33m                     **kwargs)\n\u001b[0m\u001b[0;32m   2217\u001b[0m                 \u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2218\u001b[0m                 \u001b[0mbbox_inches\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[1;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[0;32m    505\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    506\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mprint_png\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilename_or_obj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 507\u001b[1;33m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    508\u001b[0m         \u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    509\u001b[0m         \u001b[0moriginal_dpi\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    428\u001b[0m             \u001b[1;31m# if toolbar:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    429\u001b[0m             \u001b[1;31m#     toolbar.set_cursor(cursors.WAIT)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 430\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    431\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    432\u001b[0m             \u001b[1;31m# if toolbar:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     54\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 55\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     56\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     57\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m   1297\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1298\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[1;32m-> 1299\u001b[1;33m                 renderer, self, artists, self.suppressComposite)\n\u001b[0m\u001b[0;32m   1300\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1301\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'figure'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[0;32m    136\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    137\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 138\u001b[1;33m             \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    139\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    140\u001b[0m         \u001b[1;31m# Composite any adjacent images together\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     54\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 55\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     56\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     57\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer, inframe)\u001b[0m\n\u001b[0;32m   2435\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2436\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2437\u001b[1;33m         \u001b[0mmimage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0martists\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2438\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2439\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'axes'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[0;32m    136\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    137\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 138\u001b[1;33m             \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    139\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    140\u001b[0m         \u001b[1;31m# Composite any adjacent images together\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     54\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 55\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     56\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     57\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1131\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen_group\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1132\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1133\u001b[1;33m         \u001b[0mticks_to_draw\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_update_ticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1134\u001b[0m         ticklabelBoxes, ticklabelBoxes2 = self._get_tick_bboxes(ticks_to_draw,\n\u001b[0;32m   1135\u001b[0m                                                                 renderer)\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36m_update_ticks\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m    972\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    973\u001b[0m         \u001b[0minterval\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_view_interval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 974\u001b[1;33m         \u001b[0mtick_tups\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miter_ticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    975\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_smart_bounds\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mtick_tups\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    976\u001b[0m             \u001b[1;31m# handle inverted limits\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36miter_ticks\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    915\u001b[0m         \u001b[0mIterate\u001b[0m \u001b[0mthrough\u001b[0m \u001b[0mall\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mmajor\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mminor\u001b[0m \u001b[0mticks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    916\u001b[0m         \"\"\"\n\u001b[1;32m--> 917\u001b[1;33m         \u001b[0mmajorLocs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlocator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    918\u001b[0m         \u001b[0mmajorTicks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_major_ticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmajorLocs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    919\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_locs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmajorLocs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\dates.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1095\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1096\u001b[0m         \u001b[1;34m'Return the locations of the ticks'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1097\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrefresh\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1098\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_locator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1099\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\dates.py\u001b[0m in \u001b[0;36mrefresh\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1115\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mrefresh\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1116\u001b[0m         \u001b[1;34m'Refresh internal information based on current limits.'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1117\u001b[1;33m         \u001b[0mdmin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdmax\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mviewlim_to_dt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1118\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_locator\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_locator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdmin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdmax\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1119\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\dates.py\u001b[0m in \u001b[0;36mviewlim_to_dt\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    873\u001b[0m             \u001b[0mvmin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmax\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvmax\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    874\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 875\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mnum2date\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtz\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnum2date\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvmax\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtz\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    876\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    877\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_unit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\dates.py\u001b[0m in \u001b[0;36mnum2date\u001b[1;34m(x, tz)\u001b[0m\n\u001b[0;32m    464\u001b[0m         \u001b[0mtz\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_get_rc_timezone\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    465\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miterable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 466\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_from_ordinalf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtz\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    467\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    468\u001b[0m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\dates.py\u001b[0m in \u001b[0;36m_from_ordinalf\u001b[1;34m(x, tz)\u001b[0m\n\u001b[0;32m    277\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    278\u001b[0m     \u001b[0mix\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 279\u001b[1;33m     \u001b[0mdt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfromordinal\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mix\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtzinfo\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mUTC\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    280\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    281\u001b[0m     \u001b[0mremainder\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mix\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mOverflowError\u001b[0m: Python int too large to convert to C long"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1957d80b630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model, model_data = microsoft.create_prophet_model(days=0)\n",
    "#model, model_data = microsoft.create_prophet_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x195033543c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.plot_components(model_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 在数据中添加了每周组件。 可以忽略周末，因为交易只发生在一周内（由于市场后交易，价格在一夜之间略有变化，但差异很小，不会影响我们的分析）。 因此，本周没有趋势。 这是可以预期的，因为在足够短的时间尺度上，市场的变动基本上是随机的。 只能看到整体趋势。 即使是每年，也许我们无法辨别出很多模式。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "microsoft.weekly_seasonality=False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 时间序列中最重要的概念之一是变更点。 这些发生在二阶导数的最大值处。 如果这没有多大意义，它们是系列从增加到减少或反之亦然，或者当系列从缓慢增加到快速增加时。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Downloads\\Anaconda\\lib\\site-packages\\pystan\\misc.py:399: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  elif np.issubdtype(np.asarray(v).dtype, float):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Changepoints sorted by slope rate of change (2nd derivative):\n",
      "\n",
      "          Date  Adj. Close     delta\n",
      "361 2016-09-01   55.966886 -1.319902\n",
      "169 2015-11-27   51.353167 -1.128169\n",
      "289 2016-05-20   48.886934  0.847661\n",
      "506 2017-03-31   64.816957  0.580521\n",
      "120 2015-09-18   41.122995  0.571162\n"
     ]
    },
    {
     "ename": "OverflowError",
     "evalue": "Python int too large to convert to C long",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mOverflowError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    339\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    340\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    342\u001b[0m             \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m    236\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    237\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 238\u001b[1;33m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'png'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    239\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'retina'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m'png2x'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    240\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m    120\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    121\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 122\u001b[1;33m     \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    123\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    124\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'svg'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[0;32m   2214\u001b[0m                     \u001b[0morientation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2215\u001b[0m                     \u001b[0mdryrun\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2216\u001b[1;33m                     **kwargs)\n\u001b[0m\u001b[0;32m   2217\u001b[0m                 \u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2218\u001b[0m                 \u001b[0mbbox_inches\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[1;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[0;32m    505\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    506\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mprint_png\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilename_or_obj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 507\u001b[1;33m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    508\u001b[0m         \u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    509\u001b[0m         \u001b[0moriginal_dpi\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    428\u001b[0m             \u001b[1;31m# if toolbar:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    429\u001b[0m             \u001b[1;31m#     toolbar.set_cursor(cursors.WAIT)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 430\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    431\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    432\u001b[0m             \u001b[1;31m# if toolbar:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     54\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 55\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     56\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     57\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m   1297\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1298\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[1;32m-> 1299\u001b[1;33m                 renderer, self, artists, self.suppressComposite)\n\u001b[0m\u001b[0;32m   1300\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1301\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'figure'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[0;32m    136\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    137\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 138\u001b[1;33m             \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    139\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    140\u001b[0m         \u001b[1;31m# Composite any adjacent images together\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     54\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 55\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     56\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     57\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer, inframe)\u001b[0m\n\u001b[0;32m   2435\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2436\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2437\u001b[1;33m         \u001b[0mmimage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0martists\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2438\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2439\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'axes'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[0;32m    136\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    137\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 138\u001b[1;33m             \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    139\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    140\u001b[0m         \u001b[1;31m# Composite any adjacent images together\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     54\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 55\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     56\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     57\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1131\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen_group\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1132\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1133\u001b[1;33m         \u001b[0mticks_to_draw\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_update_ticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1134\u001b[0m         ticklabelBoxes, ticklabelBoxes2 = self._get_tick_bboxes(ticks_to_draw,\n\u001b[0;32m   1135\u001b[0m                                                                 renderer)\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36m_update_ticks\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m    972\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    973\u001b[0m         \u001b[0minterval\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_view_interval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 974\u001b[1;33m         \u001b[0mtick_tups\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miter_ticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    975\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_smart_bounds\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mtick_tups\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    976\u001b[0m             \u001b[1;31m# handle inverted limits\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36miter_ticks\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    915\u001b[0m         \u001b[0mIterate\u001b[0m \u001b[0mthrough\u001b[0m \u001b[0mall\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mmajor\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mminor\u001b[0m \u001b[0mticks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    916\u001b[0m         \"\"\"\n\u001b[1;32m--> 917\u001b[1;33m         \u001b[0mmajorLocs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlocator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    918\u001b[0m         \u001b[0mmajorTicks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_major_ticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmajorLocs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    919\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_locs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmajorLocs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\dates.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1095\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1096\u001b[0m         \u001b[1;34m'Return the locations of the ticks'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1097\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrefresh\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1098\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_locator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1099\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\dates.py\u001b[0m in \u001b[0;36mrefresh\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1115\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mrefresh\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1116\u001b[0m         \u001b[1;34m'Refresh internal information based on current limits.'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1117\u001b[1;33m         \u001b[0mdmin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdmax\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mviewlim_to_dt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1118\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_locator\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_locator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdmin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdmax\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1119\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\dates.py\u001b[0m in \u001b[0;36mviewlim_to_dt\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    873\u001b[0m             \u001b[0mvmin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmax\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvmax\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    874\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 875\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mnum2date\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtz\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnum2date\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvmax\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtz\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    876\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    877\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_unit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\dates.py\u001b[0m in \u001b[0;36mnum2date\u001b[1;34m(x, tz)\u001b[0m\n\u001b[0;32m    464\u001b[0m         \u001b[0mtz\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_get_rc_timezone\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    465\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miterable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 466\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_from_ordinalf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtz\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    467\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    468\u001b[0m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\dates.py\u001b[0m in \u001b[0;36m_from_ordinalf\u001b[1;34m(x, tz)\u001b[0m\n\u001b[0;32m    277\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    278\u001b[0m     \u001b[0mix\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 279\u001b[1;33m     \u001b[0mdt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfromordinal\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mix\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtzinfo\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mUTC\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    280\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    281\u001b[0m     \u001b[0mremainder\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mix\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mOverflowError\u001b[0m: Python int too large to convert to C long"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1957f648ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "microsoft.changepoint_date_analysis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Downloads\\Anaconda\\lib\\site-packages\\pystan\\misc.py:399: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  elif np.issubdtype(np.asarray(v).dtype, float):\n"
     ]
    },
    {
     "ename": "ConnectTimeout",
     "evalue": "HTTPSConnectionPool(host='trends.google.com', port=443): Max retries exceeded with url: /?geo=US (Caused by ConnectTimeoutError(<urllib3.connection.VerifiedHTTPSConnection object at 0x000001957F8BBC50>, 'Connection to trends.google.com timed out. (connect timeout=2)'))",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mtimeout\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\urllib3\\connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    140\u001b[0m             conn = connection.create_connection(\n\u001b[1;32m--> 141\u001b[1;33m                 (self.host, self.port), self.timeout, **extra_kw)\n\u001b[0m\u001b[0;32m    142\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\urllib3\\util\\connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[1;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[0;32m     82\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0merr\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 83\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     84\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\urllib3\\util\\connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[1;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[0;32m     72\u001b[0m                 \u001b[0msock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msource_address\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 73\u001b[1;33m             \u001b[0msock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msa\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     74\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0msock\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mtimeout\u001b[0m: timed out",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mConnectTimeoutError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[0;32m    600\u001b[0m                                                   \u001b[0mbody\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 601\u001b[1;33m                                                   chunked=chunked)\n\u001b[0m\u001b[0;32m    602\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[1;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[0;32m    345\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 346\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_conn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    347\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mSocketTimeout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mBaseSSLError\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36m_validate_conn\u001b[1;34m(self, conn)\u001b[0m\n\u001b[0;32m    849\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'sock'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# AppEngine might not have  `.sock`\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 850\u001b[1;33m             \u001b[0mconn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    851\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\urllib3\\connection.py\u001b[0m in \u001b[0;36mconnect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    283\u001b[0m         \u001b[1;31m# Add certificate verification\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 284\u001b[1;33m         \u001b[0mconn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_new_conn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    285\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\urllib3\\connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    145\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"Connection to %s timed out. (connect timeout=%s)\"\u001b[0m \u001b[1;33m%\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 146\u001b[1;33m                 (self.host, self.timeout))\n\u001b[0m\u001b[0;32m    147\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mConnectTimeoutError\u001b[0m: (<urllib3.connection.VerifiedHTTPSConnection object at 0x000001957F8BBC50>, 'Connection to trends.google.com timed out. (connect timeout=2)')",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mMaxRetryError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\requests\\adapters.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    439\u001b[0m                     \u001b[0mretries\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax_retries\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 440\u001b[1;33m                     \u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    441\u001b[0m                 )\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[0;32m    638\u001b[0m             retries = retries.increment(method, url, error=e, _pool=self,\n\u001b[1;32m--> 639\u001b[1;33m                                         _stacktrace=sys.exc_info()[2])\n\u001b[0m\u001b[0;32m    640\u001b[0m             \u001b[0mretries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\urllib3\\util\\retry.py\u001b[0m in \u001b[0;36mincrement\u001b[1;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[0;32m    387\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mnew_retry\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_exhausted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 388\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mMaxRetryError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_pool\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mResponseError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcause\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    389\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mMaxRetryError\u001b[0m: HTTPSConnectionPool(host='trends.google.com', port=443): Max retries exceeded with url: /?geo=US (Caused by ConnectTimeoutError(<urllib3.connection.VerifiedHTTPSConnection object at 0x000001957F8BBC50>, 'Connection to trends.google.com timed out. (connect timeout=2)'))",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mConnectTimeout\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-41-545053174bb9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmicrosoft\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchangepoint_date_analysis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msearch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'Microsoft profit'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\股票项目\\stocker.py\u001b[0m in \u001b[0;36mchangepoint_date_analysis\u001b[1;34m(self, search)\u001b[0m\n\u001b[0;32m    771\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    772\u001b[0m             \u001b[1;31m#获取指定字词的Google趋势并加入培训数据框\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 773\u001b[1;33m             \u001b[0mtrends\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrelated_queries\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mretrieve_google_trends\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msearch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdate_range\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    774\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    775\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mtrends\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;32mor\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mrelated_queries\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\股票项目\\stocker.py\u001b[0m in \u001b[0;36mretrieve_google_trends\u001b[1;34m(self, search, date_range)\u001b[0m\n\u001b[0;32m    681\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    682\u001b[0m         \u001b[1;31m# 设置趋势提取对象\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 683\u001b[1;33m         \u001b[0mpytrends\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTrendReq\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'en-US'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtz\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m360\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    684\u001b[0m         \u001b[0mkw_list\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0msearch\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    685\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\pytrends\\request.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, hl, tz, geo, timeout, proxies, retries, backoff_factor)\u001b[0m\n\u001b[0;32m     54\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbackoff_factor\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbackoff_factor\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     55\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mproxy_index\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 56\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcookies\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mGetGoogleCookie\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     57\u001b[0m         \u001b[1;31m# intialize widget payloads\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     58\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtoken_payload\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\pytrends\\request.py\u001b[0m in \u001b[0;36mGetGoogleCookie\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     77\u001b[0m                         geo=self.hl[-2:]),\n\u001b[0;32m     78\u001b[0m                     \u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 79\u001b[1;33m                     \u001b[0mproxies\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mproxy\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     80\u001b[0m                 ).cookies.items()))\n\u001b[0;32m     81\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mrequests\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexceptions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mProxyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\requests\\api.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(url, params, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     71\u001b[0m     \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'allow_redirects'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'get'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\requests\\api.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(method, url, **kwargs)\u001b[0m\n\u001b[0;32m     56\u001b[0m     \u001b[1;31m# cases, and look like a memory leak in others.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     57\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0msessions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 58\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     59\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     60\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\requests\\sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[0;32m    506\u001b[0m         }\n\u001b[0;32m    507\u001b[0m         \u001b[0msend_kwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 508\u001b[1;33m         \u001b[0mresp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    509\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    510\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\requests\\sessions.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, **kwargs)\u001b[0m\n\u001b[0;32m    616\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    617\u001b[0m         \u001b[1;31m# Send the request\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 618\u001b[1;33m         \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    619\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    620\u001b[0m         \u001b[1;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\requests\\adapters.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    494\u001b[0m                 \u001b[1;31m# TODO: Remove this in 3.0.0: see #2811\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    495\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreason\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNewConnectionError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 496\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0mConnectTimeout\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    497\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    498\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreason\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mResponseError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mConnectTimeout\u001b[0m: HTTPSConnectionPool(host='trends.google.com', port=443): Max retries exceeded with url: /?geo=US (Caused by ConnectTimeoutError(<urllib3.connection.VerifiedHTTPSConnection object at 0x000001957F8BBC50>, 'Connection to trends.google.com timed out. (connect timeout=2)'))"
     ]
    }
   ],
   "source": [
    "microsoft.changepoint_date_analysis(search = 'Microsoft profit')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 搜索频率图中的信号看起来比噪声更多，可能存在相关性，但问题是是否存在有意义的原因。 可以使用想要的任何搜索词，并且可能存在各种意外的相关性但只是噪音。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Downloads\\Anaconda\\lib\\site-packages\\pystan\\misc.py:399: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  elif np.issubdtype(np.asarray(v).dtype, float):\n"
     ]
    },
    {
     "ename": "ConnectTimeout",
     "evalue": "HTTPSConnectionPool(host='trends.google.com', port=443): Max retries exceeded with url: /?geo=US (Caused by ConnectTimeoutError(<urllib3.connection.VerifiedHTTPSConnection object at 0x000001957F86AE80>, 'Connection to trends.google.com timed out. (connect timeout=2)'))",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mtimeout\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\urllib3\\connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    140\u001b[0m             conn = connection.create_connection(\n\u001b[1;32m--> 141\u001b[1;33m                 (self.host, self.port), self.timeout, **extra_kw)\n\u001b[0m\u001b[0;32m    142\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\urllib3\\util\\connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[1;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[0;32m     82\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0merr\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 83\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     84\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\urllib3\\util\\connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[1;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[0;32m     72\u001b[0m                 \u001b[0msock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msource_address\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 73\u001b[1;33m             \u001b[0msock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msa\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     74\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0msock\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mtimeout\u001b[0m: timed out",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mConnectTimeoutError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[0;32m    600\u001b[0m                                                   \u001b[0mbody\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 601\u001b[1;33m                                                   chunked=chunked)\n\u001b[0m\u001b[0;32m    602\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[1;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[0;32m    345\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 346\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_conn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    347\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mSocketTimeout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mBaseSSLError\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36m_validate_conn\u001b[1;34m(self, conn)\u001b[0m\n\u001b[0;32m    849\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'sock'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# AppEngine might not have  `.sock`\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 850\u001b[1;33m             \u001b[0mconn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    851\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\urllib3\\connection.py\u001b[0m in \u001b[0;36mconnect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    283\u001b[0m         \u001b[1;31m# Add certificate verification\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 284\u001b[1;33m         \u001b[0mconn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_new_conn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    285\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\urllib3\\connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    145\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"Connection to %s timed out. (connect timeout=%s)\"\u001b[0m \u001b[1;33m%\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 146\u001b[1;33m                 (self.host, self.timeout))\n\u001b[0m\u001b[0;32m    147\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mConnectTimeoutError\u001b[0m: (<urllib3.connection.VerifiedHTTPSConnection object at 0x000001957F86AE80>, 'Connection to trends.google.com timed out. (connect timeout=2)')",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mMaxRetryError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\requests\\adapters.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    439\u001b[0m                     \u001b[0mretries\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax_retries\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 440\u001b[1;33m                     \u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    441\u001b[0m                 )\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[0;32m    638\u001b[0m             retries = retries.increment(method, url, error=e, _pool=self,\n\u001b[1;32m--> 639\u001b[1;33m                                         _stacktrace=sys.exc_info()[2])\n\u001b[0m\u001b[0;32m    640\u001b[0m             \u001b[0mretries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\urllib3\\util\\retry.py\u001b[0m in \u001b[0;36mincrement\u001b[1;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[0;32m    387\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mnew_retry\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_exhausted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 388\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mMaxRetryError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_pool\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mResponseError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcause\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    389\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mMaxRetryError\u001b[0m: HTTPSConnectionPool(host='trends.google.com', port=443): Max retries exceeded with url: /?geo=US (Caused by ConnectTimeoutError(<urllib3.connection.VerifiedHTTPSConnection object at 0x000001957F86AE80>, 'Connection to trends.google.com timed out. (connect timeout=2)'))",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mConnectTimeout\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-42-88eef364ec4d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmicrosoft\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchangepoint_date_analysis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msearch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'Microsoft Office'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\股票项目\\stocker.py\u001b[0m in \u001b[0;36mchangepoint_date_analysis\u001b[1;34m(self, search)\u001b[0m\n\u001b[0;32m    771\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    772\u001b[0m             \u001b[1;31m#获取指定字词的Google趋势并加入培训数据框\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 773\u001b[1;33m             \u001b[0mtrends\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrelated_queries\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mretrieve_google_trends\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msearch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdate_range\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    774\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    775\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mtrends\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;32mor\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mrelated_queries\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\股票项目\\stocker.py\u001b[0m in \u001b[0;36mretrieve_google_trends\u001b[1;34m(self, search, date_range)\u001b[0m\n\u001b[0;32m    681\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    682\u001b[0m         \u001b[1;31m# 设置趋势提取对象\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 683\u001b[1;33m         \u001b[0mpytrends\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTrendReq\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'en-US'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtz\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m360\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    684\u001b[0m         \u001b[0mkw_list\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0msearch\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    685\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\pytrends\\request.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, hl, tz, geo, timeout, proxies, retries, backoff_factor)\u001b[0m\n\u001b[0;32m     54\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbackoff_factor\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbackoff_factor\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     55\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mproxy_index\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 56\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcookies\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mGetGoogleCookie\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     57\u001b[0m         \u001b[1;31m# intialize widget payloads\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     58\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtoken_payload\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\pytrends\\request.py\u001b[0m in \u001b[0;36mGetGoogleCookie\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     77\u001b[0m                         geo=self.hl[-2:]),\n\u001b[0;32m     78\u001b[0m                     \u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 79\u001b[1;33m                     \u001b[0mproxies\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mproxy\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     80\u001b[0m                 ).cookies.items()))\n\u001b[0;32m     81\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mrequests\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexceptions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mProxyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\requests\\api.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(url, params, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     71\u001b[0m     \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'allow_redirects'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'get'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\requests\\api.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(method, url, **kwargs)\u001b[0m\n\u001b[0;32m     56\u001b[0m     \u001b[1;31m# cases, and look like a memory leak in others.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     57\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0msessions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 58\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     59\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     60\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\requests\\sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[0;32m    506\u001b[0m         }\n\u001b[0;32m    507\u001b[0m         \u001b[0msend_kwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 508\u001b[1;33m         \u001b[0mresp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    509\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    510\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\requests\\sessions.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, **kwargs)\u001b[0m\n\u001b[0;32m    616\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    617\u001b[0m         \u001b[1;31m# Send the request\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 618\u001b[1;33m         \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    619\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    620\u001b[0m         \u001b[1;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\requests\\adapters.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    494\u001b[0m                 \u001b[1;31m# TODO: Remove this in 3.0.0: see #2811\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    495\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreason\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNewConnectionError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 496\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0mConnectTimeout\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    497\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    498\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreason\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mResponseError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mConnectTimeout\u001b[0m: HTTPSConnectionPool(host='trends.google.com', port=443): Max retries exceeded with url: /?geo=US (Caused by ConnectTimeoutError(<urllib3.connection.VerifiedHTTPSConnection object at 0x000001957F86AE80>, 'Connection to trends.google.com timed out. (connect timeout=2)'))"
     ]
    }
   ],
   "source": [
    "microsoft.changepoint_date_analysis(search = 'Microsoft Office')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Downloads\\Anaconda\\lib\\site-packages\\pystan\\misc.py:399: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  elif np.issubdtype(np.asarray(v).dtype, float):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Price on 2018-04-26 00:00:00 = $98.17\n"
     ]
    },
    {
     "ename": "OverflowError",
     "evalue": "Python int too large to convert to C long",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mOverflowError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    339\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    340\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    342\u001b[0m             \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m    236\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    237\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 238\u001b[1;33m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'png'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    239\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'retina'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m'png2x'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    240\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m    120\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    121\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 122\u001b[1;33m     \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    123\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    124\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'svg'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[0;32m   2214\u001b[0m                     \u001b[0morientation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2215\u001b[0m                     \u001b[0mdryrun\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2216\u001b[1;33m                     **kwargs)\n\u001b[0m\u001b[0;32m   2217\u001b[0m                 \u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2218\u001b[0m                 \u001b[0mbbox_inches\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[1;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[0;32m    505\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    506\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mprint_png\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilename_or_obj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 507\u001b[1;33m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    508\u001b[0m         \u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    509\u001b[0m         \u001b[0moriginal_dpi\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    428\u001b[0m             \u001b[1;31m# if toolbar:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    429\u001b[0m             \u001b[1;31m#     toolbar.set_cursor(cursors.WAIT)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 430\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    431\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    432\u001b[0m             \u001b[1;31m# if toolbar:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     54\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 55\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     56\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     57\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m   1297\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1298\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[1;32m-> 1299\u001b[1;33m                 renderer, self, artists, self.suppressComposite)\n\u001b[0m\u001b[0;32m   1300\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1301\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'figure'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[0;32m    136\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    137\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 138\u001b[1;33m             \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    139\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    140\u001b[0m         \u001b[1;31m# Composite any adjacent images together\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     54\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 55\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     56\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     57\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer, inframe)\u001b[0m\n\u001b[0;32m   2435\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2436\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2437\u001b[1;33m         \u001b[0mmimage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0martists\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2438\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2439\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'axes'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[0;32m    136\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    137\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 138\u001b[1;33m             \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    139\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    140\u001b[0m         \u001b[1;31m# Composite any adjacent images together\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     54\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 55\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     56\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     57\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1131\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen_group\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1132\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1133\u001b[1;33m         \u001b[0mticks_to_draw\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_update_ticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1134\u001b[0m         ticklabelBoxes, ticklabelBoxes2 = self._get_tick_bboxes(ticks_to_draw,\n\u001b[0;32m   1135\u001b[0m                                                                 renderer)\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36m_update_ticks\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m    972\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    973\u001b[0m         \u001b[0minterval\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_view_interval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 974\u001b[1;33m         \u001b[0mtick_tups\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miter_ticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    975\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_smart_bounds\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mtick_tups\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    976\u001b[0m             \u001b[1;31m# handle inverted limits\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36miter_ticks\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    915\u001b[0m         \u001b[0mIterate\u001b[0m \u001b[0mthrough\u001b[0m \u001b[0mall\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mmajor\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mminor\u001b[0m \u001b[0mticks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    916\u001b[0m         \"\"\"\n\u001b[1;32m--> 917\u001b[1;33m         \u001b[0mmajorLocs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlocator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    918\u001b[0m         \u001b[0mmajorTicks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_major_ticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmajorLocs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    919\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_locs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmajorLocs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\dates.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1095\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1096\u001b[0m         \u001b[1;34m'Return the locations of the ticks'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1097\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrefresh\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1098\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_locator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1099\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\dates.py\u001b[0m in \u001b[0;36mrefresh\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1115\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mrefresh\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1116\u001b[0m         \u001b[1;34m'Refresh internal information based on current limits.'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1117\u001b[1;33m         \u001b[0mdmin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdmax\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mviewlim_to_dt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1118\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_locator\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_locator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdmin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdmax\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1119\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\dates.py\u001b[0m in \u001b[0;36mviewlim_to_dt\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    873\u001b[0m             \u001b[0mvmin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmax\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvmax\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    874\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 875\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mnum2date\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtz\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnum2date\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvmax\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtz\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    876\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    877\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_unit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\dates.py\u001b[0m in \u001b[0;36mnum2date\u001b[1;34m(x, tz)\u001b[0m\n\u001b[0;32m    464\u001b[0m         \u001b[0mtz\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_get_rc_timezone\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    465\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miterable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 466\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_from_ordinalf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtz\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    467\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    468\u001b[0m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Downloads\\Anaconda\\lib\\site-packages\\matplotlib\\dates.py\u001b[0m in \u001b[0;36m_from_ordinalf\u001b[1;34m(x, tz)\u001b[0m\n\u001b[0;32m    277\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    278\u001b[0m     \u001b[0mix\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 279\u001b[1;33m     \u001b[0mdt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfromordinal\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mix\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtzinfo\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mUTC\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    280\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    281\u001b[0m     \u001b[0mremainder\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mix\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mOverflowError\u001b[0m: Python int too large to convert to C long"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1957d7cb400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model, future = microsoft.create_prophet_model(days=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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